Hansen, Hendricks and Rendsvig (2013) introduced the term 'infostorms' for large-scale misleading socio-epistemic dynamics that arise when modern information technologies amplify pluralistic ignorance, informational cascades, and belief polarization, and they left open both the exact formal relations between information technologies and such amplification and the design principles or intervention rules that would keep technologies from misleading users away from what they have good reasons to believe. A decade of follow-up work has formalized the component phenomena (dynamic-epistemic-logic models of pluralistic ignorance and cascades, threshold-diffusion and term-modal logics of social networks, social-learning and algorithmic-bias opinion models), has empirically documented online amplification (echo chambers, faster spread of falsehood, distorted norm perception), and has tested behavioral interventions (accuracy prompts, sharing friction) - but no unified formal account connects platform design parameters to amplification of the three phenomena, and no intervention principles with reliability guarantees exist. The problem as posed therefore remains open: build the missing formal account of the amplification relations and the control layer it is meant to deliver.

Background

In social settings, what an agent believes and does depends not only on private evidence but on what the agent can observe of others. Three group-level pathologies of such social information processing have been studied since the mid-twentieth century. Pluralistic ignorance is a configuration in which most members of a group privately reject a belief or norm but wrongly believe that most others accept it, so each member's public conformity perpetuates the misperception (canonical examples: bystander non-intervention in emergencies and misperceived attitudes on campus). An informational cascade arises in sequential decision settings - modeled classically by Banerjee (1992) and Bikhchandani, Hirshleifer and Welch (1992) - when later agents, observing predecessors' actions but not their private signals, rationally ignore their own evidence and copy the emerging behavioral pattern; cascades aggregate information poorly, can start on very weak evidence, and yet are fragile to a single sufficiently informative contrarian action. Belief polarization (group polarization) is the experimentally robust finding that deliberation among like-minded people shifts the group's mean position toward a more extreme one, driven by informational influence (arguments pooled on one side) and social comparison (presenting oneself favorably relative to peers). All three mechanisms run on social proof - treating other agents' observable behavior and expressed views as evidence about the world - and on what agents take to be mutually known among them.

Hansen, Hendricks and Rendsvig (2013) coupled these phenomena to a hypothesis about technology: modern information technologies - social-network feeds, ranking and recommendation algorithms, personalization, and visible engagement metrics such as likes and upvotes - shortcut the slow gatekeeping and search processes that once limited the scale of misinformation-like dynamics, and can amplify pluralistic ignorance, informational cascades, and belief polarization to unprecedented reach, producing what they call infostorms: large-scale, misleading socio-epistemic dynamics that lead users away from what they have good reasons to believe. The article developed this claim conceptually, with case studies and the canonical models of each phenomenon, and argued that infostorms should be treated as information-control problems, analogous to engineering control problems: one must identify the components that generate the failure - private and public signals, observable actions, agents' preferences and decision rules, the interpretive rules they apply to others' behavior, and the underlying network structure - and then apply formal tools such as epistemic logic (the logical study of knowledge and belief attribution, including what agents know about each other's knowledge), game theory, and decision theory to derive control and mitigation strategies. But the article supplied exactly that framing and no more: the amplification hypothesis itself was presented as a conceptual hypothesis supported by anecdotal case studies rather than by formal models, simulations, or causal estimates, and no worked-out intervention formulation was given. The article closes by pointing to 'the need for studying further the exact relations between information technologies and such infostorms, as well as the ways we may design technologies to avoid being misled away from what we have good reasons to believe'.

Problem Statement

The problem posed by Hansen, Hendricks and Rendsvig (2013), stated in their own terms, has two jointly posed parts.

(1) Amplification account. Construct a formal account of the exact relations between information technologies and infostorms: a framework - using tools such as epistemic logic, game theory, decision theory, or network and opinion-dynamics models - in which pluralistic ignorance, informational cascades, and belief polarization are all explicitly represented for agents connected through an information technology, and in which the technology's design parameters (network and feed architecture, social-proof signals such as rankings, likes, recommendations, and visible engagement metrics, and visibility or moderation rules) are explicit variables. Within this framework, characterize when and how these design parameters amplify the three phenomena - the conditions on signals, decision rules, interpretive rules, and network structure under which the technology magnifies their reach, stability, and social impact, and the mechanisms responsible. For each of the three phenomena the account must exhibit at least one explicit amplification condition - a stated condition on the design parameters, or on the signals, decision rules, interpretive rules, or network structure through which they act, under which the technology magnifies that phenomenon's reach, stability, or social impact - together with the mechanism responsible.

(2) Design and control. Derive from that account design principles or intervention rules - changes to signals, decision rules, or network structure - that demonstrably prevent or mitigate infostorms, in the sense of keeping users' belief formation aligned with what they have good reasons to believe (operationalizing this epistemic criterion is itself part of the task, but the criterion must be stated explicitly and non-trivially, as a stated measure of belief formation relative to the agents' evidence, such as correctness or calibration with any tolerance made explicit), and validate them. The demonstration that an intervention succeeds must be given within the model and relative to the stated criterion.

The account is to cover the three phenomena within one framework and to connect the amplification analysis to the proposed interventions.

References

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